316 Awardee Talk: Development and Assessment of Predictive Models for Improved Swine Farming

Abstract

Abstract Prediction of outcomes is critical in swine breeding and management. This necessitates the development of predictive models that address challenges in swine farming. For predictive modeling, there have been significant advances in machine learning. Nevertheless, there are needs to adapt predictive models for specific swine farming problems including genomic prediction and behavior analysis. Furthermore, there is not yet a clear guideline on how to validate novel models in these fields. The overarching goal of this work was to validate a collection of predictive models for improved swine farming with applications to precision management, phenotyping, and breeding. The first study addressed the pig genomic prediction problem. Differential evolution algorithm was utilized to optimize hyperparameters of deep learning (DL) models, which affected the predictive performance of DL. Performance of optimized DL was compared with “best practice” DL architectures selected from literature and baseline DL models with randomly specified hyperparameters. Optimized models showed clear improvement. Despite the success of genomic prediction, phenotyping has become a bottleneck in breeding programs as it is still time-consuming and labor-intensive. The second study aimed at collecting a video dataset to study agonistic behavior of pigs and adapting a state-of-the-art DL pipeline to classify agonistic behaviors of pigs through video analysis. The pipeline was validated through various training-validation data partitions, where the training data were used for model development and the validation data were used for model evaluation. Results showed that splitting the training and validation sets at random led to over-optimistic estimates of model performance. Social behavior continues to be an important topic in animal behavior studies. However, less attention was paid to the modeling of animal social behavior. The last study focused on developing and validating a statistical model for the analysis of social interactions of pigs. Generalized linear mixed models were fitted, and a Bayesian framework was used for parameter estimation and posterior predictive model checking. The predictive performance of the models varied depending on the validation strategy, where three strategies were defined: random cross-validation, block-by-social-group cross-validation, and block-by-focal-animals validation. Collectively, the research items presented how state-of-the-art predictive models can be adapted for and validated in swine farming applications. The findings are also informative to livestock farming in general, as genomic prediction and animal social behavior (of group-housed animals) are popular topics in livestock species.